Payment Fraud Risk Scoring With Pre-Approval User Alerts
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Solution Overview
Problem
Conventional payment systems fail to adequately protect users from fraudulent transactions, exposing them to financial losses due to insufficient fraud detection measures.
Innovation Solution
Implementing real-time fraud risk scoring during transactions using a machine learning model, generating alerts to users and financial institutions about potential fraud, and providing options to cancel or override transactions before fund transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time fraud risk scoring is implemented during transactions, then user protection against fraudulent transactions is improved, but transaction processing time increases
Solution Approach 1:
The system performs fraud risk scoring and generates alerts before the transaction is completed and funds are transferred. This preliminary action allows users to cancel potentially fraudulent transactions before they are finalized, providing protection without requiring post-transaction remediation.
Solution Approach 2:
The fraud detection system acts as an intermediary between the payment system and the user, providing risk assessment information that enables informed decision-making. This intermediary layer adds protection while maintaining user control over the transaction outcome.
2Reliability
If fraud detection alerts are provided to users during transactions, then fraudulent transactions are reduced, but user experience complexity increases
Solution Approach 1:
The system provides users with the information and tools needed to make their own fraud detection decisions. Users receive alerts with risk information and can independently decide whether to proceed with or cancel the transaction, rather than having the system make the decision for them.
Solution Approach 2:
The system provides real-time feedback to users about the fraud risk level of a transaction through alerts. This feedback loop enables users to adjust their behavior based on the risk information provided, improving fraud detection while maintaining user autonomy.
3Measurement precision
If machine learning models are used for fraud risk scoring, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system replaces traditional rule-based fraud detection mechanisms with machine learning models. This substitution enables more accurate detection of fraudulent patterns by leveraging the adaptive learning capabilities of ML algorithms rather than relying on static rules.
Solution Approach 2:
The system uses transaction information from known fraudulent transactions to train the machine learning model. By learning from historical patterns and creating a digital representation of fraud characteristics, the model achieves high detection accuracy without requiring complex manual analysis of each transaction.
Data Source
AI summary
A payment and authentication network may include a communications interface, one or more processors, and a memory. The memory may have instructions stored thereon that, when executed by the one or more processors cause the one or more processors to receive, using the communications interface, transaction information associated with a transaction from a merchant system and generate a fraud risk score based on the transaction information. The instructions may cause the one or more processors to determine that the fraud risk score is indicative that the transaction is likely fraudulent and transmit an alert to a user device that informs a user of the user device that the transaction is likely fraudulent prior to generating an approval decision for the transaction.